[目的]峰丛洼地是重要的喀斯特地貌类型之一,探明不同景观位置对Ks的影响规律可为深入理解该区水文过程提供参考。[方法]通过测定2种景观位置不同土层Ks和土壤理化性质,采用方差分析、回归分析和通径分析等方法研究不同景观位置Ks的分布特征及其影响因素。[结果]景观位置和土层对Ks分布有显著的影响(p0.05)。景观位置对土壤颗粒组成和全磷影响不明显(p>0.05),而有机碳、全氮和体积质量随景观位置不同发生明显变化(p<0.05)。坡地Ks与粉粒、砂粒、有机碳和全磷显著正相关(p<0.05),与黏粒和体积质量呈显著负相关(p<0.05);洼地Ks与体积质量呈显著正相关(p<0.05)。通径分析表明,体积质量和有机碳是影响坡地和洼地Ks的主要因素。利用多元逐步回归建立坡地和洼地Ks的回归方程,其影响因素方差解释率分别为76.2%和32.6%。[结论]地貌特征分异对土壤水文参数分布有重要影响,可为喀斯特峰丛洼地小流域水土过程研究提供科学依据。

Understanding the characteristics of flood resistance within agricultural ecosystems is essential for the planning of agricultural structures within karst depressions against floods. However, the responses of crops to flooding were rarely investigated under natural precipitation extremes in karst depressions. This study investigates the influence of flooding on agricultural ecosystems (Maize and Napier grass) in a typical karst peak-cluster depression in Southwest China after an extreme rainfall event in 2016. Results showed that this rainfall event was once every 10 years, and caused 78.6 % of Maize and 20.4 % of Napier grass in death. Napier grass performed better than Maize when submerged, due to higher fine root biomass and the stable photosynthetic capacity of the leaves. The submergence critical tolerance time of flooding to Napier grass was 10.2 days; only when the Maize plant protruded 1.5 m above the water level can it survive. In response to this extreme event in 2016, the safe planting height for Napier grass and Maize was 3.09 m and 6.21 m, respectively, relative to the lowest point of the depression elevation (set at 0.00 m). This study implicated that it is necessary to rationally arrange the layout of agroecosystems at the karst depression according to the ability of plants to withstand flooding to cope with future climate change.

Soil water storage (SWS) is a crucial parameter for vegetation restoration and a variety of hydrological processes in semi -arid regions. Its dynamics reflect the integrated influence of different factors operating at various intensities and scales. In this study, we investigated the scale -specific correlations between SWS and six selected environmental factors along a 1340-m long sampling transect in a semi -arid catchment using multivariate empirical mode decomposition (MEMD). The six factors were bulk density, elevation, clay, silt, sand, and soil organic carbon content. The multivariate data series of the SWS and factors of interest were separated into seven intrinsic mode functions (IMFs) and residue. IMF1, IMF3, and IMF5 were identified as the primary scales according to the percentage of total variance (about 50%) in SWS under different soil moisture conditions. The scale -specific correlations between SWS and the corresponding factors varied with scale but were irrelevant to soil moisture condition. SWS at each IMF and residue could be estimated using the associated scale -specific environmental factors at the same IMF or residue. SWS at the measurement scale was satisfactorily estimated by summarizing all the predicted IMFs and residue, which outperformed measurement -scale SWS estimation based on multivariate stepwise linear regression between SWS and the associated factors. Soil moisture condition could affect the relative importance of the environmental factors in overall SWS estimation. In general, soil properties were the dominant predictor of SWS under different soil moisture conditions. MEMD provides a useful opportunity to deal with scale -specific correlations between various variables.

The cosmic-ray neutron sensing (CRNS) is an emerging method for continuously monitoring soil water content (SWC) at an intermediate scale. However, when multiple hydrologic units are present within its footprint, the potential application of CRNS in water resources management is restricted. Here we propose a new strategy to predict point-scale SWC in root zone established on CRNS-based soil moisture and improved relative difference method. A total of 768 days of soil moisture data were collected by CRNS at the intermediate scale and EC-TM sensors at the point scale in a karst catchment. The original and improved mean relative difference methods predicted point-scale SWCs within and without the effective measuring depth, respectively. The mean effective measuring depth was 13.16 cm, ranging from 10.13 to 19.23 cm. Both land use type and soil structure played essential roles in regulating point scale SWC in the soil profile. Point-scale SWC in root zone can be predicted accurately (P © 2023 The Authors

Spatiotemporal variability in soil water storage (SWS) is controlled by different factors operating at various intensities and scales. The traditional Pearson's correlation analysis can be used to identify the linear correlations at the measurement scale only. In this study, wavelet coherency analysis was used to investigate scale-specific relationships between SWS and selected controlling factors. SWS of 135 sampling locations were calculated along a 1,340-m long sampling transect established in a typical catchment on the Loess Plateau, China. The selected controlling factors were soil saturated hydraulic conductivity (Ks); clay, silt, sand, and soil organic carbon (SOC) contents; elevation; and aboveground biomass (AGB). The spatial pattern of SWS measured in growing and nongrowing seasons and at different soil depths was similar. At all the sampling points, except for some locations in the depression area, SWS in the growing season was relatively greater than that in the nongrowing season. The influence of factors on SWS varied with scale, with soil Ks, clay and sand contents significantly correlated with SWS at large scales. Season and soil depth had no significant effect on scale-specific relationships between SWS and the controlling factors. The wavelet coherency analysis identified the type of correlation at different scales and locations. The outcomes of this study offer meaningful insights into the hydrological processes that can be observed in the Loess Plateau region and could have significant implications for future hydrological systems and water resources management.

Soil water storage (SWS) dynamics are affected by many factors in semiarid regions such as the Loess Plateau. Spatiotemporal variation of SWS varies greatly with different landscape positions due to the rolling and fragmented terrain landscape of the Loess Plateau. However, information on the SWS and its temporal stability at different landscape positions is lacking. In this study, hillslope, dam land and gully landscape positions were selected in a typical small watershed in the Loess Plateau. A soil water observation belt with 28 sample locations was set up at each landscape position, and temporal stability analysis was conducted using SWS data of 12 sampling events. The results showed that the variation characteristics of profile SWS are influenced by landscape position. The persistence of spatial SWS patterns in different landscape positions increases with the increase of soil depth. On each landscape position, representative locations can accurately predict their SWS based on temporal stability analysis. Moreover, the stability of SWS at the representative locations was more important than its content during the prediction process. The dominant factors that lead to the difference of temporal stability characteristics in different landscape positions varied with the change in soil depths. The results provide an important basis for vegetation reconstruction and water management in small Loess Plateau watersheds.

Knowledge of spatiotemporal dynamics of soil water storage (SWS) is essential for hydrological modeling and vegetation restoration in semi-arid areas. However, characterizing the temporal stability of SWS at a regional scale requires time-consuming and labor-intensive manual sampling. Moreover, the influence of soil depth on temporal stability of SWS is not systematic. This study aimed to investigate the influences of sampling frequency and soil depth on SWS and SWS temporal stability. We measured soil moisture at 20-cm intervals in the soil profiles to a depth of 3 m using a neutron probe at 135 locations along a 1340-m long transect on 14 dates from 2012 to 2013. Results showed that sampling frequency did not influence the mean SWS (P < 0.05), while sampling frequency significantly affected temporal stability characteristics including Spearman's rank correlation coefficient (r(s)), standard deviation of mean relative difference (SDRD), the number of locations with SDRD < 5%, and the representative locations. Temporal stability of SWS increased with the increasing soil thickness and depth, which increases the possibility of the number of representative locations in deep soil. Although the mean SWSs of all soil depths can be predicted accurately at each sampling frequency, the prediction accuracy improved when sampling frequency was reducing. The values of R-2 ranged from 0.769 to 0.978 at 15-day sampling frequency, and from 0.987 to 0.998 at 45-day sampling frequency. Soil moisture stability may be more important than the soil water regime during prediction of soil moisture. These findings can provide guidelines for optimizing soil moisture sampling strategies and benefit management of water resources in semiarid watershed.

厘清饱和水汽压差（VPD）的年际动态特征有助于深入理解大气干湿程度对气候变化的响应。本研究基于我国西南地区105个台站1982—2015年的气象观测资料，利用Mann-Kendall突变检验分析VPD的突变特征，基于统计分析探讨突变点前后的VPD变化趋势及影响因素。研究结果表明，西南地区1982—2015年VPD变化以2000年为突变年，增速由0.001 kPa/a变为0.005 kPa/a，并且VPD呈增加趋势的站点在2000年后增多。VPD在时间动态上出现变化的主要原因是在2000—2015年间相对湿度下降幅度增加，导致西南地区出现更干燥的气候特征。作为大气主要水汽来源的外来水汽通量和局地实际蒸散发在2000年后表现为增长趋势，但由于2000年后更高的空气温度引起饱和水汽压比实际水汽压增幅更大，即大气中实际水汽增加远低于饱和水汽容量增加，进而导致VPD增幅更大。本研究表明在突变点后西南地区VPD显著变化的原因主要是温度上升对相对湿度的影响，研究结果有助于深入理解中国西南地区的气候变化，明晰VPD变化背后的相关机制，为该地区的生态系统构建和未来气候变化应对提供参考。